How Does Data Science Fuel the FinTech Revolution?

This growing cross-industry valued at almost $20 billion started with the first attempt to use computers for financial purposes such as tax computation or portfolio creation but due to the growth of Big Data applications, the industry has blossomed in the past five years. However, an interesting observation was made by techrevolution regarding the link between technology and finance: it grows stronger after each significant recession since people are more motivated to find new ways to hedge against risks and they are turning to statistics and models. In fact, fintech would not exist without data analysis, data science, and other connected domains.

Data science applications for Fintech

The value of fintech companies is given by the life-changing and business-enhancing applications they develop, which is usually their value proposition. The main categories are related to transaction facilitation, investments and portfolio management as well as marketing purposes, but others will emerge as the domain grows.

Transactional

By analyzing large volumes of past records, fintech companies can create behavioral patterns and anticipate future trends. One of the most useful applications is a personal spending advisor. The app measures each client’s payments, then splits them into categories and triggers warnings when a certain threshold is surpassed. Another use can be the integration of credit cards with payment channels to streamline and automate these steps, saving the client some time and ensuring they are never late, thus increasing their credit score.

Third-party retail companies could also be interested in having these records to customize their offer by targeting just the right clients and creating laser-sharp loyalty programs. The rise of the e-commerce has increased the importance of such tools that replace personal interaction found in offline environments.

Particular attention goes to evaluating the accuracy of transactions and fraud prevention. Also, through pattern analysis, it is now possible to prevent identity theft and detect misbehavior just by looking at the metadata connected to each transaction. Since people are creatures of habit, any sudden changes in the amounts transferred, the device used, or the geolocation of the request could trigger additional verification measures. These tools are already in use and yielding positive results.

Investments

One of the critical issues of the finance sector is the risk evaluation before portfolio building or giving credit. Fintech companies have come forward with numerous solutions. Through machine learning algorithms, financial institutions can use the terabytes of data they have from current transactions to teach robot-advisers to minimize loss and automatically create hedged portfolios. This approach also helps cut down expenses since scenarios can be generated and tested in seconds, without the need to make a real investment.

It is worth noticing that although most decisions are based on patterns derived from numbers, data science offers the opportunity to use sentiment analysis as an innovative way to evaluate assets since the general feeling of the market towards one company can significantly influence the stock variation.

One fintech pioneer, Kabbage, has transformed the personal loans business by letting go of the traditional evaluation methods like the FICO score or the Vantage credit rating. Instead, they collect approximately 15,000 data points for each applicant, including transaction history, social network, studies and much more to create a 360 degree view of their prospective clients.

Client retention

Having the right data can help a company estimate the exact lifetime value of each client and even determine their journey. This is important as it can be used as a base to direct promotions and marketing resources most efficiently. Also, if there are specific signs that the client intends to end the relationship such as late payments or being unresponsive to communication, it could mean that they need additional reasons to continue working with the company.

Although not all the data companies collect from their clients is useful from a financial point of view, it can still hold valuable insights about preferences, behaviors, and things that might influence their buying decision, and therefore may be useful for marketing purposes. Even improving an existing recommendation engine by including these new data points will translate into earnings later, so each set of data should be used to its maximum potential.

Challenges of implementing data science

Although “data scientist” was voted as the best job of 2017, one of the leading problems is to find the right experts for your specific project. There is currently no track program for this qualification, most experts come from engineering, applied mathematics or statistics. Even if they have excellent capabilities, the learning curve is steep, since there are no predefined ways to tackle problems. It makes more economic sense to collaborate with a data consulting company instead of building an in-house team — of course, unless, you are Google or Amazon.

Another unique challenge is finding the right balance between security and speed. Protection is given by using end-to-end data encryption. Yet, using this can slow down communication and make crime fighting impossible. Yet a lot of experts and consulting companies advise that this is the only way we can limit the exposure of personal data and prevent identity thefts or other attacks.

Last but not least, collecting and cleaning the data is perhaps the most crucial challenge. For this you need a clear strategy in place defining what type of data you need, where does it come from, how often do you record it and if it needs additional work.

A disruptive trend

The current role of fintech is to be a disruptive force bringing innovations into a petrified sector like banking and finance. The most important contribution of fintech is that is giving people and companies control over their assets and decisions, removing some of the black-box approaches that banks had until now.

Data is the fuel powering the fintech revolution, but it’s useless unless carefully distilled by specialists into actionable insights that can help companies lower their risks, detect and prevent fraud, manage assets and even create plans to maintain long term relationships with clients.